Characterization of genetic variation and dissection of complex genetic architectures of complex traits or diseases have been a great challenge. Family-based gene-mapping approaches have shown tremendous success for rare monogenic diseases, but are underpowered to detect genes underling complex diseases. Furthermore, with advance in high throughput technologies, many previous methods cannot adequately handle new data because of lack of efficiency or violation of assumptions. In this project, we seek to develop efficient algorithms to tackle the inheritance inference problem in large pedigrees. The algorithms relies on a framework developed previously by our group that can effectively handle pair-wise identical by decent (IBD) inference from large pedigrees with many untyped members. We will also apply the new algorithms on a real dataset consisting of 20 large complex pedigrees and perform analysis on recombination breakpoint identification, haplotype inference, family-based linkage and association studies based on haplotype segments separated by recombination breakpoints, joint analysis of SNP data and sequence data in families. All our developments will focus on practically important issues, including large pedigrees with many untyped members, genotyping errors, scalability issues on high-throughput datasets. Software tools will be developed and will be made available to the public.